1 Challenge the future Features of Point Clouds and Functionalities of Processing Software Mathias...

1Challenge the future

Features of Point Clouds and Functionalities of Processing Software

Mathias J.P.M. LemmensDelft University of Technology, The Netherlands (MSc

Geomatics for the Built Environment: (1) Geodata Acquisition Technology &

(2) Geodata Quality)

Senior Editor GIM InternationalInternational Consultant (2014 -2015: WB, Kenya)[email protected]

mailto:[email protected]


GIM International senior editor Mathias Lemmens is guiding his TU Delft students through Intergeo, Berlin, 2014.

“It’s very encouraging to know that the community is so big.”

“The first day our professor guided us along a variety of booths and showed us how to fearlessly shoot questions and ask for demonstrations.”


Published by Springer, 2011


Nucleus of Point Clouds


Shukhov Tower Moscow, Russia

Built in 1919-1922, radio broadcasting

Monument of Russian avant-garde architecture

100 million points accuracy: 7mm

Mikhail Anikushkin & Andrey Leonov, Russia, 3D Modelling of Shukhov Tower3D Modelling of Shukhov Tower, GIM Int’l, July 2014


Terrasolid, Road Maintenance with MMS, pilot in Finland in 2012, GIM Int’l, July 2014

Mobile Mapping System

MMS data processed with sophisticated software is well suited for road maintenance (+ machine steering) tasks as many parameters can be accurately calculated from the 3D model.

Section of 22 km of the two-lane NR6 (Finland) with Trimble MX8: 2 Riegl VQ-250 scanners 4 cameras – 1 recording road surface and 3pointing forward Applanix POS LV 520


• 40% below sea-level

• Threats from Sea, Germany (Rhine) and France (Meuse)

• Height Model

• 1998: 1 pnt / 25 square meters

• 2013: 10 pnts / square meter

On Average doubling of number of points every two years.

Airborne Lidar

Netherlands


Aerial multi camera systems capture oblique and nadir imagery at the same time full and intuitive view on both building footprints and facades beneficial for creating 3D city models.

Object identification and creation of dense 3D point clouds are easier and more reliable compared to conventional vertical imagery although the cost of capturing is higher.


Dense image matching allows point densities similar to the ground sampling distance (GSD) of the imagery from which they are derived. (e.g. GSD of 10cm 100 height points per square meter.

Semi-global matching (SGM) algorithm introduced by Hirschmüller (2008)

Challenging for oblique imagery: - large scale variations - illumination changes - many occlusions

Need for performance measures of DIM software


Task Photo-

Scan

Stereo-

SGBM

SURE Xpro

SGM

Image rectification V

V

Image matching V V V V

Point cloud generation

V

V V

DSM computation V

V

DSM texturingV

V

M. Deuber, S. Cavegn, S. Nebiker (Switzerland) Dense Image Matching – Performance Analysis on Oblique Imagery, GIM Int’l, Sept. 2014.


CompletenessRatio between number of pixels to which software assigns a depth value and total number of pixels.


Massive Point Clouds Everywhere

What to do with all those points? Benchmark

EuroSDR Survey - Oblique Airborne Photogrammetry: Users’ and Vendor’s ViewMarkus Gerke, University of Twente, The NetherlandsFabio Remondino, FBK Trento, Italy(To be published in GIM Int’l Dec. 2014)

Massive Point clouds for eSciences; Delft University of Technology; Rijkswaterstaat; Netherlands eScience Centre; Fugro and Oracle(Submitted to Computers & Graphics)


EuroSDRSurvey

130 Respondents; Universities 45%, National Mapping Agencies 21%Questionnaire still open https://www.surveymonkey.com/s/EuroSDR_oblique

What can obliques do better?

https://www.surveymonkey.com/s/EuroSDR_oblique

https://www.surveymonkey.com/s/EuroSDR_oblique


• Point clouds are too massive for efficient handling by common Geo-ICT infrastructures

• User requirements by structured interviews with users.

• Based on requirements a benchmark has been designed by comparing the loading and querying of data sets consisting of 20 million, 20 billion and 640 billion points

• Oracle, PostgreSQL, MonetDB and LAStools.

• Proposals for storage improvement and thus accessibility

Massive Point Clouds for eSciences


Sources of Point Clouds

• Millions, billions, trillions of 3D points

• Airborne Lidar

• Overlapping imagery (Dense Image Matching)

• Terrestrial Laser Scanning / Mobile Mapping

• Spaceborne and Airborne Radar (e.g. Terrasat)

• Sonar


FunctionalitiesOf Point CloudProcessing software


Software

General purpose: handle point clouds from a diversity of sensors (e.g. Terrasolid)

Dedicated to specific output e.g. TLS, airborne Lidar, MMS or sonar (proprietary software)

Focus may be on a broad pallet of end products from a particular sensor type

Other end of the spectrum: application domains. Constructor using CAD exploits TLS point clouds and want modules for processing them. Dedicated modules on top of base modules, e.g. mining industry or 3D models of crash sites.


Development far from complete

New tools are being added all the time

A generic package for all types of sensor outputs end products does not exist

Look at functionalities but also examine design ideas, current or planned extensions, its ability to join modules into one workflow, and interoperability with other software and services.


To compare Point Cloud Processing Software have a look at the successor of GIM’s Product Surveys


Point Clouds – Acquisition, Processing and Management

to be published by Whittless Publishing, UK, in 2016.

To be presented at ISPRS Congress, 2016, Prague.


Thank you so much for your attention.

1 Challenge the future Features of Point Clouds and Functionalities of Processing Software Mathias...

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